Akanksha Makkar Email: ad4axv@r.postjobfree.com

Seeking Internship Opportunities Phone: +1-971-***-**** EDUCATION

M.S., Electrical & Computer Engg. University of California, Los Angeles CPI: 4.0/4.0 Sept. 2023 – Mar. 2025 B. Tech, Electrical Engineering Indian Institute of Technology, Kanpur CPI: 9.1/10.0 Aug. 2016 – May 2020 TECHNICAL SKILLS

Programming Languages

JAVA - Spring Boot, Hibernate Python - PyTorch, Keras, Scikit-learn C++ (OOPS) Software Development & Data Management

DBMS - PostgreSQL, MySQL, Redis Cache Container Management - Docker, Kubernetes API Development - RESTful APIs Event Streaming - Kafka, Redis Streams Monitoring & Alerts - Prometheus, Grafana API Testing - Swagger, Postman Cloud Computing - AWS, Snowflake Data Warehousing - Dimensional Modeling DB - Mysql, Postgres, MongoDb Data Science & Machine Learning

Data Visualization - Seaborn, Matplotlib Statistical Modeling - StatsModel, Sklearn Feature Engineering - Pandas WORK EXPERIENCE

Software Developer, NAVI Technologies (April 2022 - April 2023) o Worked in the Super-App team to build and maintain microservices in JAVA using Spring Boot Framework o Revamped the Deep-Linking service to redirect contextual and deferred deep-links, removing dependency on Branch o Developed a client with Kafka Event Listeners for sending whitelisted events to Marketing Platforms using their remote APIs o Implemented campaign allocation jobs via Athena Queries, built payment client using Protobuf event queuing for Rewards o Improved monitoring and alerting system by creating metrics and alerts using promQL and Grafana for high priority services Quantitative Analyst, Goldman Sachs (July 2020 – April 2022) o Worked with a multi-tier team across the world for 18 months in Prime Services of Global Markets (Securities) division o Developed a Mosek based Linear Optimization pricing tool in C++, automating breakeven spreads for future index trades o Improved speed of Synthetics Inventory Optimizer by 10 times to minimize inefficiencies in book for EMEA and Asia in JAVA o Designed an Inventory Blotter for traders to have a real-time view of positions, trade data, and asset information o Analyzed trends by generating a dynamic P&L Drivers Report using TSDB to get historical curves of various financial metrics o Built a tree data structure and parser to query aggregated data according to the applied filter, improving speed by 60 times PROJECTS

Movie Recommendation System Mentor: Prof. Roychowdhury, UCLA (Jan. 2024 – Feb. 2024) o Compared recommendation results for Matrix Factorization, KNN, Naive Collaborative Filter Models on MovieLens Dataset. o Used LightGBM model with the ’lambdarank’ objective to achieve nDCG of 0.49 on Web10k data News Article Classification: End-to-end Pipeline Mentor: Prof. Roychowdhury, UCLA (Jan. 2024 – Feb. 2024) o Built an end-to-end pipeline for text vectorization, dimensionality reduction, multi-class classification to tag New Articles. o Implemented grid search to optimize hyperparameters for techniques used in steps above to achieve 93% testing accuracy. Text based Image Rendering Mentor: Prof. Achuta Kadambi, UCLA (Sept. 2023 – Nov. 2023) o Implemented Stable Diffusion with CLIP as text encoder to generate images based on text prompt o Compared and contrasted outputs using different variational autoencoders, schedulers and guidance scales. Subspace Frank-Wolfe Optimization Mentor: Prof. Ketan Rajawat, IIT Kanpur (Aug. 2019 – Nov. 2019) o Proposed a novel stochastic descent algorithm for constrained optimization avoiding projection onto the constrained set o Mathematically proved the convergence of subspace Frank-Wolfe algorithm in O(1/ϵ) iteration complexity o Implemented the technique to verify the convergence & iteration complexity and proved similar or better than existing ways Graph Representation Learning, Machine Learning Mentor: Prof. Piyush Rai, IIT Kanpur (Aug. 2018 – Nov. 2018) o Implemented low dimensional embeddings on graphs like GraphSAGE, Node2Vec & GCN to capture its structural information o Analyzed performance of the methods on node classification & clustering of Cora Citation Network with 98, 97, 99% accuracy o Applied the methods to solve the problem of node classification and clustering on graph i.e finding community in a graph Quantized Matrix Completion for Personalised Learning Mentor: Prof. Vipul Arora, IIT Kanpur (Nov. 2018 – Dec. 2018) Mentor: Prof. Vipul Arora, Department of Electrical Engineering, IIT Kanpur o Studied SPARFA-lite algorithm to learn learner’s concept-knowledge profile and predict unobserved learner responses o Compared SPARFA-lite with SPARFA based on evaluation metrics like AUC Score, Prediction Likelihood & Accuracy RELEVANT COURSEWORK

Computational Imaging Big Data Optimization Large-Scale Data Mining Deeplearning.ai